Orthogonal tensor dictionary learning for accelerated dynamic MRI.
A direct application of the compressed sensing (CS) theory to dynamic magnetic resonance imaging (MRI) reconstruction needs vectorization or matricization of the dynamic MRI data, which is composed of a stack of 2D images and can be naturally regarded as a tensor. This 1D/2D model may destroy the in...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 57; no. 9; pp. 1933 - 1947 |
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| Autores principales: | , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Sep2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=138201472&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 138201472 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Sep2019 vid: 57 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 138201472 138201472 144093399 NLM31254175 10.1007/s11517-019-02005-x NLM31254175 138201472 ppf: 1933 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Orthogonal tensor dictionary learning for accelerated dynamic MRI. aug: au: Huang, Jinhong Zhou, Genjiao Yu, Gaohang affil: School of Mathematics and Computer Science, Gannan Normal University, Ganzhou, China sug: subj: Image Processing, Computer Assisted Methods Algorithms Magnetic Resonance Imaging Methods Phantoms, Imaging Multidimensional Health Locus of Control Scales Scales ab: A direct application of the compressed sensing (CS) theory to dynamic magnetic resonance imaging (MRI) reconstruction needs vectorization or matricization of the dynamic MRI data, which is composed of a stack of 2D images and can be naturally regarded as a tensor. This 1D/2D model may destroy the inherent spatial structure property of the data. An alternative way to exploit the multidimensional structure in dynamic MRI is to employ tensor decomposition for dictionary learning, that is, learning multiple dictionaries along each dimension (mode) and sparsely representing the multidimensional data with respect to the Kronecker product of these dictionaries. In this work, we introduce a novel tensor dictionary learning method under an orthonormal constraint on the elementary matrix of the tensor dictionary for dynamic MRI reconstruction. The proposed algorithm alternates sparse coding, tensor dictionary learning, and updating reconstruction, and each corresponding subproblem is efficiently solved by a closed-form solution. Numerical experiments on phantom and synthetic data show significant improvements in reconstruction accuracy and computational efficiency obtained by the proposed scheme over the existing method that uses the 1D/2D model with overcomplete dictionary learning. Graphical abstract Fig. 1 Comparison between (a) the traditional method and (b) the proposed method based on dictionary learning for dynamic MRI reconstruction. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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